Sentiment Classification of Anxiety-Related Texts in Social Media via Fuzing Linguistic and Semantic Features

Abstract

Anxiety disorder is a common mental disorder that has received increasing attention due to its high incidence, comorbidity, and recurrence. In recent years, with the rapid development of information technology, social media platforms have become a crucial source of data for studying anxiety disorders. Existing studies on anxiety disorders have focused on utilizing user-generated contents to study correlations with disorders or identify disorders. However, these studies overlook the emotional information in social media posts, restraining the effective capture of users’ emotions or mental states when posting. This article focuses on the sentiment polarity of anxiety-related posts on a Chinese social media and designs sentiment classification models via fuzing linguistic and semantic features of the posts. First, we extract the linguistic features from posts based on the simplified Chinese–Linguistic inquiry and word count (SC-LIWC) dictionary, and propose a novel recursive feature selection algorithm to reserve important linguistic features. Second, we propose a TextCNN-based model to study the deep semantic features of posts and fuze their linguistic features to obtain a better representation. Finally, to conduct anxiety analysis on Chinese social media, we construct a postlevel sentiment analysis dataset based on anxiety-related posts on Sina Weibo. The experimental results indicate that our proposed fusion models exhibit better performance in the task of identifying the sentiment polarity of anxiety-related posts on Chinese social media.